Citation

Wang W, Chen L, Tan M, Wang S, Sheth AP. Biomed. Inform. Insights 2012; 5(Suppl 1): 137-145.

Affiliation

Kno.e.sis Center, Wright State University, Dayton, USA.

Copyright

(Copyright © 2012, Libertas Academica)

DOI

10.4137/BII.S8963

PMID

22879770

PMCID

PMC3409482

Abstract

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.

Language: en